project1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## World Progress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, you'll explore data from [Gapminder.org](http://gapminder.org), a website dedicated to providing a fact-based view of the world and how it has changed. That site includes several data visualizations and presentations, but also publishes the raw data that we will use in this project to recreate and extend some of their most famous visualizations.\n",
    "\n",
    "The Gapminder website collects data from many sources and compiles them into tables that describe many countries around the world. All of the data they aggregate are published in the [Systema Globalis](https://github.com/open-numbers/ddf--gapminder--systema_globalis/blob/master/README.md). Their goal is \"to compile all public statistics; Social, Economic and Environmental; into a comparable total dataset.\" All data sets in this project are copied directly from the Systema Globalis without any changes.\n",
    "\n",
    "This project is dedicated to [Hans Rosling](https://en.wikipedia.org/wiki/Hans_Rosling) (1948-2017), who championed the use of data to understand and prioritize global development challenges.\n",
    "\n",
    "### Logistics\n",
    "\n",
    "**Deadline.** This project is due at 11:59pm on Thursday 9/28. Late submissions will be accepted until Sunday 10/1, but a 33.33% late penalty will be applied for each day late. It's **much** better to be early than late, so start working now.\n",
    "\n",
    "**Checkpoint.** For full credit, you must also complete the first 8 questions and submit them by 11:59pm on Friday 9/22. You will have some lab time to work on these questions, but we recommend that you start the project before lab and leave time to finish the checkpoint afterward.\n",
    "\n",
    "**Partners.** You may work with one other partner. It's best to work with someone in your lab. Only one of you is required to submit the project. On [okpy.org](http://okpy.org), the person who submits should also designate their partner so that both of you receive credit.\n",
    "\n",
    "**Rules.** Don't share your code with anybody but your partner. You are welcome to discuss questions with other students, but don't share the answers. The experience of solving the problems in this project will prepare you for exams (and life). If someone asks you for the answer, resist! Instead, you can demonstrate how you would solve a similar problem.\n",
    "\n",
    "**Support.** You are not alone! Come to office hours, post on Piazza, and talk to your classmates. If you want to ask about the details of your solution to a problem, make a private Piazza post and the staff will respond. If you're ever feeling overwhelmed or don't know how to make progress, email your TA or tutor for help. You can find contact information for the staff on the [course website](http://data8.org/sp17/staff.html).\n",
    "\n",
    "**Tests.** Passing the tests for a question **does not** mean that you answered the question correctly. Tests usually only check that your table has the correct column labels. However, more tests will be applied to verify the correctness of your submission in order to assign your final score, so be careful and check your work!\n",
    "\n",
    "**Advice.** Develop your answers incrementally. To perform a complicated table manipulation, break it up into steps, perform each step on a different line, give a new name to each result, and check that each intermediate result is what you expect. You can add any additional names or functions you want to the provided cells. \n",
    "\n",
    "To get started, load `datascience`, `numpy`, `plots`, and `ok`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=====================================================================\n",
      "Assignment: World Progress\n",
      "OK, version v1.12.5\n",
      "=====================================================================\n",
      "\n",
      "Successfully logged in as kcs1992@berkeley.edu\n"
     ]
    }
   ],
   "source": [
    "from datascience import *\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plots\n",
    "plots.style.use('fivethirtyeight')\n",
    "\n",
    "from client.api.notebook import Notebook\n",
    "ok = Notebook('project1.ok')\n",
    "_ = ok.auth(inline=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "## 1. Global Population Growth\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "The global population of humans reached 1 billion around 1800, 3 billion around 1960, and 7 billion around 2011. The potential impact of exponential population growth has concerned scientists, economists, and politicians alike.\n",
    "\n",
    "The UN Population Division estimates that the world population will likely continue to grow throughout the 21st century, but at a slower rate, perhaps reaching 11 billion by 2100. However, the UN does not rule out scenarios of more extreme growth.\n",
    "\n",
    "<a href=\"http://www.pewresearch.org/fact-tank/2015/06/08/scientists-more-worried-than-public-about-worlds-growing-population/ft_15-06-04_popcount/\"> \n",
    " <img src=\"pew_population_projection.png\"/> \n",
    "</a>\n",
    "\n",
    "In this section, we will examine some of the factors that influence population growth and how they are changing around the world."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "The first table we will consider is the total population of each country over time. Run the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>geo</th> <th>time</th> <th>population_total</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>abw </td> <td>1800</td> <td>19286           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>abw </td> <td>1801</td> <td>19286           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>abw </td> <td>1802</td> <td>19286           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (87792 rows omitted)</p"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The population.csv file can also be found online here:\n",
    "# https://github.com/open-numbers/ddf--gapminder--systema_globalis/raw/master/ddf--datapoints--population_total--by--geo--time.csv\n",
    "# The version in this project was downloaded in February, 2017.\n",
    "population = Table.read_table('population.csv')\n",
    "population.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "### Bangladesh\n",
    "\n",
    "In the `population` table, the `geo` column contains three-letter codes established by the [International Organization for Standardization](https://en.wikipedia.org/wiki/International_Organization_for_Standardization) (ISO) in the [Alpha-3](https://en.wikipedia.org/wiki/ISO_3166-1_alpha-3#Current_codes) standard. We will begin by taking a close look at Bangladesh. Inspect the standard to find the 3-letter code for Bangladesh."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 1.** Create a table called `b_pop` that has two columns labeled `time` and `population_total`. The first column should contain the years from 1970 through 2015 (including both 1970 and 2015) and the second should contain the population of Bangladesh in each of those years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 65048701,  71247153,  81364176,  93015182, 105983136, 118427768,\n",
       "       131280739, 142929979, 151616777, 160995642])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bgd = population.where('geo', are.equal_to('bgd')).where('time', are.between(1970, 2016))\n",
    "\n",
    "bgd.column('population_total')[::5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>time</th> <th>population_total</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1970</td> <td>65048701        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1971</td> <td>66417450        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1972</td> <td>67578486        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1973</td> <td>68658472        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1974</td> <td>69837960        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1975</td> <td>71247153        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1976</td> <td>72930206        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1977</td> <td>74848466        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1978</td> <td>76948378        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1979</td> <td>79141947        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (36 rows omitted)</p"
      ],
      "text/plain": [
       "time | population_total\n",
       "1970 | 65048701\n",
       "1971 | 66417450\n",
       "1972 | 67578486\n",
       "1973 | 68658472\n",
       "1974 | 69837960\n",
       "1975 | 71247153\n",
       "1976 | 72930206\n",
       "1977 | 74848466\n",
       "1978 | 76948378\n",
       "1979 | 79141947\n",
       "... (36 rows omitted)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bgd = population.where('geo', are.equal_to('bgd')).where('time', are.between(1970, 2016))\n",
    "\n",
    "b_pop = bgd.drop('geo')\n",
    "b_pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "deletable": false,
    "editable": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 3\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.grade('q1_1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Run the following cell to create a table called `b_five` that has the population of Bangladesh every five years. At a glance, it appears that the population of Bangladesh has been growing quickly indeed!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>time</th> <th>population_total</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1970</td> <td>65,048,701      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1975</td> <td>71,247,153      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1980</td> <td>81,364,176      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1985</td> <td>93,015,182      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1990</td> <td>105,983,136     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1995</td> <td>118,427,768     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2000</td> <td>131,280,739     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2005</td> <td>142,929,979     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2010</td> <td>151,616,777     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2015</td> <td>160,995,642     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "time | population_total\n",
       "1970 | 65,048,701\n",
       "1975 | 71,247,153\n",
       "1980 | 81,364,176\n",
       "1985 | 93,015,182\n",
       "1990 | 105,983,136\n",
       "1995 | 118,427,768\n",
       "2000 | 131,280,739\n",
       "2005 | 142,929,979\n",
       "2010 | 151,616,777\n",
       "2015 | 160,995,642"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_pop.set_format('population_total', NumberFormatter)\n",
    "\n",
    "fives = np.arange(1970, 2016, 5) # 1970, 1975, 1980, ...\n",
    "b_five = b_pop.sort('time').where('time', are.contained_in(fives))\n",
    "b_five"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 2.** Create a table called `b_five_growth` that includes three columns, `time`, `population_total`, and `annual_growth`. There should be one row for every five years from 1970 through 2010 (but not 2015). The first two columns are the same as `b_five`. The third column is the **annual** growth rate for each five-year period. For example, the annual growth rate for 1975 is the yearly exponential growth rate that describes the total growth from 1975 to 1980 when applied 5 times. (Consult the [Growth Rates](https://www.inferentialthinking.com/chapters/03/3/1/example-growth-rates.html) example in the textbook to help understand how to compute the **annual** growth rate)\n",
    "\n",
    "*Hint*: Only your `b_five_growth` table will be scored for correctness; the other names are suggestions that you are welcome to use, change, or delete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>time</th> <th>population_total</th> <th>annual_growth</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1970</td> <td>65,048,701      </td> <td>1.84%        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1975</td> <td>71,247,153      </td> <td>2.69%        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1980</td> <td>81,364,176      </td> <td>2.71%        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1985</td> <td>93,015,182      </td> <td>2.64%        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1990</td> <td>105,983,136     </td> <td>2.25%        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1995</td> <td>118,427,768     </td> <td>2.08%        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2000</td> <td>131,280,739     </td> <td>1.71%        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2005</td> <td>142,929,979     </td> <td>1.19%        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2010</td> <td>151,616,777     </td> <td>1.21%        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "time | population_total | annual_growth\n",
       "1970 | 65,048,701       | 1.84%\n",
       "1975 | 71,247,153       | 2.69%\n",
       "1980 | 81,364,176       | 2.71%\n",
       "1985 | 93,015,182       | 2.64%\n",
       "1990 | 105,983,136      | 2.25%\n",
       "1995 | 118,427,768      | 2.08%\n",
       "2000 | 131,280,739      | 1.71%\n",
       "2005 | 142,929,979      | 1.19%\n",
       "2010 | 151,616,777      | 1.21%"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_1970_through_2010 = np.arange(1970, 2011, 5)\n",
    "initial = b_five.column('population_total')[:-1]\n",
    "changed = b_five.column('population_total')[1:]\n",
    "temp = (changed/initial) ** (1/5) - 1\n",
    "b_five_growth = b_five.where('time', are.below(2015)).with_column('annual_growth', temp)\n",
    "\n",
    "\n",
    "b_five_growth.set_format('annual_growth', PercentFormatter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 4\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.grade('q1_2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "While the population has grown every five years since 1970, the annual growth rate decreased dramatically from 1985 to 2005. Let's look at some other information in order to develop a possible explanation. Run the next cell to load three additional tables of measurements about countries over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": true,
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "life_expectancy = Table.read_table('life_expectancy.csv')\n",
    "child_mortality = Table.read_table('child_mortality.csv').relabeled(2, 'child_mortality_under_5_per_1000_born')\n",
    "fertility = Table.read_table('fertility.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "The `life_expectancy` table contains a statistic that is often used to measure how long people live, called *life expectancy at birth*. This number, for a country in a given year, [does not measure how long babies born in that year are expected to live](http://blogs.worldbank.org/opendata/what-does-life-expectancy-birth-really-mean). Instead, it measures how long someone would live, on average, if the *mortality conditions* in that year persisted throughout their lifetime. These \"mortality conditions\" describe what fraction of people at each age survived the year. So, it is a way of measuring the proportion of people that are staying alive, aggregated over different age groups in the population."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 3.** Perhaps population is growing more slowly because people aren't living as long. Use the `life_expectancy` table to draw a line graph with the years 1970 and later on the horizontal axis that shows how the *life expectancy at birth* has changed in Bangladesh."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "deletable": false,
    "manual_grade": true,
    "manual_problem_id": "global_pop_3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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O9+7dE3lMRkYGnJycsGfPHgwePLjWcycl1b1fDyFEuhiGQfLrTDyKf4W/n6eiqLjmeqj/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\nADDnA4Zyn8dt/ifDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe226c8bba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bgd = life_expectancy.where('geo', are.equal_to('bgd')).where('time',are.above_or_equal_to(1970)).drop('geo')\n",
    "bgd.plot(0,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 4.** Does the graph above help directly explain why the population growth rate decreased from 1985 to 2010 in Bangladesh? Why or why not? What happened in Bangladesh in 1991, and does that event explain the change in population growth rate?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": false,
    "manual_grade": true,
    "manual_problem_id": "global_pop_4"
   },
   "source": [
    "*It does not explain why the population growth rate decreased from 1985 to 2010 since the slope of the graph is increasing except 1991 when cyclone happened that caused a lot of death in 1991 and changed in population growth rate during that time. *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "The `fertility` table contains a statistic that is often used to measure how many babies are being born, the *total fertility rate*. This number describes the [number of children a woman would have in her lifetime](https://www.measureevaluation.org/prh/rh_indicators/specific/fertility/total-fertility-rate), on average, if the current rates of birth by age of the mother persisted throughout her child bearing years, assuming she survived through age 49. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 5.** Write a function `fertility_over_time` that takes the Alpha-3 code of a `country` and a `start` year. It returns a two-column table with labels \"`Year`\" and \"`Children per woman`\" that can be used to generate a line chart of the country's fertility rate each year, starting at the `start` year. The plot should include the `start` year and all later years that appear in the `fertility` table. \n",
    "\n",
    "Then, in the next cell, call your `fertility_over_time` function on the Alpha-3 code for Bangladesh and the year 1970 in order to plot how Bangladesh's fertility rate has changed since 1970. Note that the function `fertility_over_time` should not return the plot itself **The expression that draws the line plot is provided for you; please don't change it.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>time</th> <th>children_per_woman_total_fertility</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1970</td> <td>7.67                              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1971</td> <td>7.67                              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1972</td> <td>7.67                              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1973</td> <td>7.67                              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1974</td> <td>7.67                              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1975</td> <td>7.67                              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1976</td> <td>7.67                              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1977</td> <td>7.67                              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1978</td> <td>7.67                              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1979</td> <td>7.67                              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (9232 rows omitted)</p"
      ],
      "text/plain": [
       "time | children_per_woman_total_fertility\n",
       "1970 | 7.67\n",
       "1971 | 7.67\n",
       "1972 | 7.67\n",
       "1973 | 7.67\n",
       "1974 | 7.67\n",
       "1975 | 7.67\n",
       "1976 | 7.67\n",
       "1977 | 7.67\n",
       "1978 | 7.67\n",
       "1979 | 7.67\n",
       "... (9232 rows omitted)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fertility.where('time',are.above_or_equal_to(1970)).where('children_per_woman_total_fertility').drop('geo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": true,
    "deletable": false
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def fertility_over_time(country, start):\n",
    "    fertility = Table.read_table('fertility.csv')\n",
    "    fert = fertility.relabeled('children_per_woman_total_fertility', 'Children per woman' )\n",
    "    fert = fert.relabeled('time', 'Year' )\n",
    "    \"\"\"Create a two-column table that describes a country's total fertility rate each year.\"\"\"\n",
    "    country_fertility = fert.where('Year',are.above_or_equal_to(start)).where('geo',are.equal_to(country)).drop('geo')\n",
    "    \n",
    "    return country_fertility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Year</th> <th>Children per woman</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>2010</td> <td>1.93              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2011</td> <td>1.9               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2012</td> <td>1.9               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2013</td> <td>1.98              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2014</td> <td>1.97              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2015</td> <td>1.97              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Year | Children per woman\n",
       "2010 | 1.93\n",
       "2011 | 1.9\n",
       "2012 | 1.9\n",
       "2013 | 1.98\n",
       "2014 | 1.97\n",
       "2015 | 1.97"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fertility_over_time('usa', 2010)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
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GYaCkpIR169Zh/fr15a6TlpZWo4GR+smsiR4WzxiGDXtPo6i4hN9+6NRlyMhIo0PbFiKM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4a9b07390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bangladesh_code = 'bgd'\n",
    "fertility_over_time(bangladesh_code, 1970).plot(0, 1) # You should *not* change this line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 2\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.grade('q1_5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 6.** Does the graph above help directly explain why the population growth rate decreased from 1985 to 2010 in Bangladesh? Why or why not?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "manual_grade": true,
    "manual_problem_id": "global_pop_6"
   },
   "source": [
    "*The graph clearly shows the slope is decreasing, meaning that the childeren per woman is decreasing from 1985 to 2010 and one of the reasons could be due to the child mortality rate. As the year increases, the better treatment came up and saved a lot of children. Thus, the graph seems to help directly explain the population growth rate decreased. *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "It has been observed that lower fertility rates are often associated with lower child mortality rates. The link has been attributed to family planning: if parents can expect that their children will all survive into adulthood, then they will choose to have fewer children. We can see if this association is evident in Bangladesh by plotting the relationship between total fertility rate and [child mortality rate per 1000 children](https://en.wikipedia.org/wiki/Child_mortality)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 7.** Using both the `fertility` and `child_mortality` tables, draw a scatter diagram with one point for each year, starting with 1970, that has Bangladesh's total fertility on the horizontal axis and its child mortality on the vertical axis. \n",
    "\n",
    "**The expression that draws the scatter diagram is provided for you; please don't change it.** Instead, create a table called `fertility_and_child_mortality` with the appropriate column labels and data in order to generate the chart correctly. Use the label \"`Children per woman`\" to describe total fertility and the label \"`Child deaths per 1000 born`\" to describe child mortality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#fert = fertility.where('geo', 'bgd').where('time', are.above_or_equal_to(1970))\n",
    "#child = child_mortality.where('geo', 'bgd').where('time', are.above_or_equal_to(1970))\n",
    "def mortality_over_time(country, start):\n",
    "    child = child_mortality.where('geo', 'bgd').where('time', are.above_or_equal_to(1970)).drop('geo')\n",
    "    child = child.relabeled('child_mortality_under_5_per_1000_born', 'Child deaths per 1000 born')\n",
    "\n",
    "    \"\"\"Create a two-column table that describes a country's total fertility rate each year.\"\"\"\n",
    "    #country_fertility = fert.where('Year',are.above_or_equal_to(start)).where('geo',are.equal_to(country)).drop('geo')\n",
    "    \n",
    "    return child"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ipKSEg65ERC8R1YIPDg6Gi4sLevToAQBYsmQJvvjiCzg7OyMsLAwSiQTvvfee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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4b1588ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_fertility = fertility_over_time('bgd', 1970)\n",
    "country_child_mortality = mortality_over_time('bgd', 1970)\n",
    "fertility_and_child_mortality = country_fertility.join(\"Year\", country_child_mortality, \"time\")\n",
    "fertility_and_child_mortality.scatter('Children per woman', 'Child deaths per 1000 born') # You should *not* change this line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Year</th> <th>Children per woman</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1970</td> <td>6.95              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1971</td> <td>6.94              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1972</td> <td>6.93              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1973</td> <td>6.9               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1974</td> <td>6.87              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1975</td> <td>6.82              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1976</td> <td>6.76              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1977</td> <td>6.68              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1978</td> <td>6.59              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1979</td> <td>6.48              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (36 rows omitted)</p"
      ],
      "text/plain": [
       "Year | Children per woman\n",
       "1970 | 6.95\n",
       "1971 | 6.94\n",
       "1972 | 6.93\n",
       "1973 | 6.9\n",
       "1974 | 6.87\n",
       "1975 | 6.82\n",
       "1976 | 6.76\n",
       "1977 | 6.68\n",
       "1978 | 6.59\n",
       "1979 | 6.48\n",
       "... (36 rows omitted)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_fertility\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>time</th> <th>Child deaths per 1000 born</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1970</td> <td>224.1                     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1971</td> <td>223                       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1972</td> <td>222                       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1973</td> <td>220.7                     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1974</td> <td>219.1                     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1975</td> <td>217.1                     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1976</td> <td>214.5                     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1977</td> <td>211.2                     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1978</td> <td>207.4                     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1979</td> <td>203.3                     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (36 rows omitted)</p"
      ],
      "text/plain": [
       "time | Child deaths per 1000 born\n",
       "1970 | 224.1\n",
       "1971 | 223\n",
       "1972 | 222\n",
       "1973 | 220.7\n",
       "1974 | 219.1\n",
       "1975 | 217.1\n",
       "1976 | 214.5\n",
       "1977 | 211.2\n",
       "1978 | 207.4\n",
       "1979 | 203.3\n",
       "... (36 rows omitted)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "child = child_mortality.where('geo', 'bgd').where('time', are.above_or_equal_to(1970)).drop('geo')\n",
    "child = child.relabeled('child_mortality_under_5_per_1000_born', 'Child deaths per 1000 born')\n",
    "child\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 2\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.grade('q1_7')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 8.** In one or two sentences, describe the association (if any) that is illustrated by this scatter diagram. Does the diagram show that reduced child mortality causes parents to choose to have fewer children?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false
   },
   "source": [
    "*There exists association between them since child deaths increase as fertility increaes*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "### Congratulations: Checkpoint Reached\n",
    "\n",
    "You have reached the project checkpoint. Please submit now in order to record your progress. If you go back and revise your answers in the section above after the checkpoint is due, that's ok. Your revised answers will be graded. However, you will only get credit for your checkpoint submission if you have passed the tests provided for every question above.\n",
    "\n",
    "If you are working with a partner, only one of you needs to submit. For both of you to receive credit, the person who submits must invite the other to be their partner on [okpy.org](http://okpy.org). Please invite your partner now and tell them to accept the invitation **before** the checkpoint deadline!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.save_checkpoint();"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = ok.submit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "### The World\n",
    "\n",
    "The change observed in Bangladesh since 1970 can also be observed in many other developing countries: health services improve, life expectancy increases, and child mortality decreases. At the same time, the fertility rate often plummets, and so the population growth rate decreases despite increasing longevity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Run the cell below to generate two overlaid histograms, one for 1960 and one for 2010, that show the distributions of total fertility rates for these two years among all 201 countries in the `fertility` table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HJyIiIt1RuwBxdXXFzZs3NRKioqICe/bsQXFxMXr27Inc3Fzk5eXBx8dHuY+pqSn69OmD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\nYwFCREREWscChIiIiLSOBQgRERFp3f8C5vQiA0n7oRQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb106fd73c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Table().with_columns(\n",
    "    '1960', fertility.where('time', 1960).column(2),\n",
    "    '2010', fertility.where('time', 2010).column(2)\n",
    ").hist(bins=np.arange(0, 10, 0.5), unit='child')\n",
    "_ = plots.xlabel('Children per woman')\n",
    "_ = plots.xticks(np.arange(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 9.** Assign `fertility_statements` to a list of the numbers for each statement below that can be correctly inferred from these histograms.\n",
    "1. About the same number of countries had a fertility rate between 3.5 and 4.5 in both 1960 and 2010.\n",
    "1. In 2010, about 40% of countries had a fertility rate between 1.5 and 2 (inclusive).\n",
    "1. In 1960, less than 20% of countries had a fertility rate below 3.\n",
    "1. More countries had a fertility rate above 3 in 1960 than in 2010.\n",
    "1. At least half of countries had a fertility rate between 5 and 8 (inclusive) in 1960.\n",
    "1. At least half of countries had a fertility rate below 3 in 2010."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "deletable": false
   },
   "outputs": [],
   "source": [
    "fertility_statements = make_array(2,4,5,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 1\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.grade('q1_9')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 10.** Draw a line plot of the world population from 1800 through 2005. The world population is the sum of all the country's populations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "deletable": false,
    "manual_grade": true,
    "manual_problem_id": "global_pop_10"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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51JmYvvjii+aMg4iIHvHznmOoqKhSLdvbWWNssL+EETUPPmMiItJBKTczEBVzSa0sdPxQ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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe21b627f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop = population.where('time', are.between(1800,2006)).group('time', sum).drop('geo')\n",
    "pop\n",
    "#sum(pop.where('population_total'))\n",
    "\n",
    "pop.plot(0,2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 11.** Create a function `stats_for_year` that takes a `year` and returns a table of statistics. The table it returns should have four columns: `geo`, `population_total`, `children_per_woman_total_fertility`, and `child_mortality_under_5_per_1000_born`. Each row should contain one Alpha-3 country code and three statistics: population, fertility rate, and child mortality for that `year` from the `population`, `fertility` and `child_mortality` tables. Only include rows for which all three statistics are available for the country and year.\n",
    "\n",
    "In addition, restrict the result to country codes that appears in `big_50`, an array of the 50 most populous countries in 2010. This restriction will speed up computations later in the project.\n",
    "\n",
    "*Hint*: The tests for this question are quite comprehensive, so if you pass the tests, your function is probably correct. However, without calling your function yourself and looking at the output, it will be very difficult to understand any problems you have, so try your best to write the function correctly and check that it works before you rely on the `ok` tests to confirm your work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": false
   },
   "outputs": [],
   "source": [
    "# We first create a population table that only includes the \n",
    "# 50 countries with the largest 2010 populations. We focus on \n",
    "# these 50 countries only so that plotting later will run faster.\n",
    "big_50 = population.where('time', 2010).sort(2, descending=True).take(np.arange(50)).column('geo')\n",
    "population_of_big_50 = population.where('time', are.above(1959)).where('geo', are.contained_in(big_50))\n",
    "\n",
    "def stats_for_year(year):\n",
    "    \"\"\"Return a table of the stats for each country that year.\"\"\"\n",
    "    p = population_of_big_50.where('time', year).drop('time')\n",
    "    f = fertility.where('time', year).drop('time')\n",
    "    c = child_mortality.where('time', year).drop('time')\n",
    "    return p.join('geo',f,'geo').join('geo',c,'geo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try calling your function `stats_for_year` on any year between 1960 and 2010 in the cell below.  Try to understand the output of `stats_for_year`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2624944597"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(stats_for_year(1960).column('population_total'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 4\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.grade('q1_11')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 12.** Create a table called `pop_by_decade` with two columns called `decade` and `population`. It has a row for each `year` since 1960 that starts a decade. The `population` column contains the total population of all countries included in the result of `stats_for_year(year)` for the first `year` of the decade. For example, 1960 is the first year of the 1960's decade. You should see that these countries contain most of the world's population.\n",
    "\n",
    "*Hint:* One approach is to define a function `pop_for_year` that computes this total population, then `apply` it to the `decade` column.  The `stats_for_year` function from the previous quesiton may be useful here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def pop_for_year(year):\n",
    "    return sum(stats_for_year(year).column('population_total'))\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This test is just a sanity check for your helper function if you choose to use it. You will not lose points for not implementing the function pop_for_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 1\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.grade('q1_12_0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>decade</th> <th>population_total</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1960  </td> <td>1               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1970  </td> <td>2               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1980  </td> <td>3               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1990  </td> <td>4               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2000  </td> <td>5               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2010  </td> <td>6               </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "decade | population_total\n",
       "1960   | 1\n",
       "1970   | 2\n",
       "1980   | 3\n",
       "1990   | 4\n",
       "2000   | 5\n",
       "2010   | 6"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decades = Table().with_column('decade', np.arange(1960, 2011, 10))\n",
    "decades.with_column('population_total', make_array(1,2,3,4,5,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>decade</th> <th>population</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1960  </td> <td>2,624,944,597</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1970  </td> <td>3,211,487,418</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1980  </td> <td>3,880,722,003</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>1990  </td> <td>4,648,434,558</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2000  </td> <td>5,367,553,063</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2010  </td> <td>6,040,810,517</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "decade | population\n",
       "1960   | 2,624,944,597\n",
       "1970   | 3,211,487,418\n",
       "1980   | 3,880,722,003\n",
       "1990   | 4,648,434,558\n",
       "2000   | 5,367,553,063\n",
       "2010   | 6,040,810,517"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decades = Table().with_column('decade', np.arange(1960, 2011, 10))\n",
    "                  \n",
    "pop_by_decade = decades.with_column('population', decades.apply(pop_for_year, 'decade'))\n",
    "pop_by_decade.set_format(1, NumberFormatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 2\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.grade('q1_12')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "The `countries` table describes various characteristics of countries. The `country` column contains the same codes as the `geo` column in each of the other data tables (`population`, `fertility`, and `child_mortality`). The `world_6region` column classifies each country into a region of the world. Run the cell below to inspect the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>country</th> <th>name</th> <th>world_6region</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>afg      </td> <td>Afghanistan          </td> <td>south_asia              </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>akr_a_dhe</td> <td>Akrotiri and Dhekelia</td> <td>europe_central_asia     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>alb      </td> <td>Albania              </td> <td>europe_central_asia     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>dza      </td> <td>Algeria              </td> <td>middle_east_north_africa</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>asm      </td> <td>American Samoa       </td> <td>east_asia_pacific       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>and      </td> <td>Andorra              </td> <td>europe_central_asia     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>ago      </td> <td>Angola               </td> <td>sub_saharan_africa      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>aia      </td> <td>Anguilla             </td> <td>america                 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>atg      </td> <td>Antigua and Barbuda  </td> <td>america                 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>arg      </td> <td>Argentina            </td> <td>america                 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (245 rows omitted)</p"
      ],
      "text/plain": [
       "country   | name                  | world_6region\n",
       "afg       | Afghanistan           | south_asia\n",
       "akr_a_dhe | Akrotiri and Dhekelia | europe_central_asia\n",
       "alb       | Albania               | europe_central_asia\n",
       "dza       | Algeria               | middle_east_north_africa\n",
       "asm       | American Samoa        | east_asia_pacific\n",
       "and       | Andorra               | europe_central_asia\n",
       "ago       | Angola                | sub_saharan_africa\n",
       "aia       | Anguilla              | america\n",
       "atg       | Antigua and Barbuda   | america\n",
       "arg       | Argentina             | america\n",
       "... (245 rows omitted)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = Table.read_table('countries.csv').where('country', are.contained_in(population.group('geo').column(0)))\n",
    "countries.select('country', 'name', 'world_6region')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 13.** Create a table called `region_counts` that has two columns, `region` and `count`. It should describe the count of how many countries in each region appear in the result of `stats_for_year(1960)`. For example, one row would have `south_asia` as its `world_6region` value and an integer as its `count` value: the number of large South Asian countries for which we have population, fertility, and child mortality numbers from 1960."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>geo</th> <th>population_total</th> <th>children_per_woman_total_fertility</th> <th>child_mortality_under_5_per_1000_born</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>afg </td> <td>8994793         </td> <td>7.67                              </td> <td>362.4                                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>arg </td> <td>20619075        </td> <td>3.11                              </td> <td>73.2                                 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>bgd </td> <td>48200702        </td> <td>6.73                              </td> <td>264.3                                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>bra </td> <td>72493585        </td> <td>6.21                              </td> <td>171.8                                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>can </td> <td>17909232        </td> <td>3.91                              </td> <td>32.6                                 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>chn </td> <td>644450173       </td> <td>3.99                              </td> <td>309                                  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>cod </td> <td>15248246        </td> <td>6                                 </td> <td>266                                  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>col </td> <td>16480384        </td> <td>6.81                              </td> <td>127.3                                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>deu </td> <td>73179665        </td> <td>2.41                              </td> <td>42.87                                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>dza </td> <td>11124892        </td> <td>7.65                              </td> <td>245.7                                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (40 rows omitted)</p"
      ],
      "text/plain": [
       "geo  | population_total | children_per_woman_total_fertility | child_mortality_under_5_per_1000_born\n",
       "afg  | 8994793          | 7.67                               | 362.4\n",
       "arg  | 20619075         | 3.11                               | 73.2\n",
       "bgd  | 48200702         | 6.73                               | 264.3\n",
       "bra  | 72493585         | 6.21                               | 171.8\n",
       "can  | 17909232         | 3.91                               | 32.6\n",
       "chn  | 644450173        | 3.99                               | 309\n",
       "cod  | 15248246         | 6                                  | 266\n",
       "col  | 16480384         | 6.81                               | 127.3\n",
       "deu  | 73179665         | 2.41                               | 42.87\n",
       "dza  | 11124892         | 7.65                               | 245.7\n",
       "... (40 rows omitted)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_for_year(1960)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>region</th> <th>count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>america                 </td> <td>8    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>east_asia_pacific       </td> <td>10   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>europe_central_asia     </td> <td>10   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>middle_east_north_africa</td> <td>7    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>south_asia              </td> <td>5    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>sub_saharan_africa      </td> <td>10   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "region                   | count\n",
       "america                  | 8\n",
       "east_asia_pacific        | 10\n",
       "europe_central_asia      | 10\n",
       "middle_east_north_africa | 7\n",
       "south_asia               | 5\n",
       "sub_saharan_africa       | 10"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_counts = countries.select('country', 'name', 'world_6region').join('country',stats_for_year(1960), 'geo').group('world_6region').relabeled('world_6region', 'region')\n",
    "#region_counts.where('country',are.containing(stats_for_year(1960)))\n",
    "region_counts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 2\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.grade('q1_13')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "The following scatter diagram compares total fertility rate and child mortality rate for each country in 1960. The area of each dot represents the population of the country, and the color represents its region of the world. Run the cell. Do you think you can identify any of the dots?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bcDwkJAQAb29vNBoNw4cPB3J608LCwpgyZQre3t507NgRgKioKIKCgnB3d6d2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\n3Mj169cZPHgwPj4+hIWFATB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enU4LgC5bhS7RFd2tGuhueeX8n+CJ7oEGvT4ngbty9zzX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\ndevGmjVrf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb10c117e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from functools import lru_cache as cache\n",
    "\n",
    "# This cache annotation makes sure that if the same year\n",
    "# is passed as an argument twice, the work of computing\n",
    "# the result is only carried out once.\n",
    "@cache(None)\n",
    "def stats_relabeled(year):\n",
    "    \"\"\"Relabeled and cached version of stats_for_year.\"\"\"\n",
    "    return stats_for_year(year).relabeled(2, 'Children per woman').relabeled(3, 'Child deaths per 1000 born')\n",
    "\n",
    "def fertilty_vs_child_mortality(year):\n",
    "    \"\"\"Draw a color scatter diagram comparing child mortality and fertility.\"\"\"\n",
    "    with_region = stats_relabeled(year).join('geo', countries.select('country', 'world_6region'), 'country')\n",
    "    with_region.scatter(2, 3, sizes=1, colors=4, s=500)\n",
    "    plots.xlim(0,10)\n",
    "    plots.ylim(-50, 500)\n",
    "    plots.title(year)\n",
    "\n",
    "fertilty_vs_child_mortality(1960)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 14.** Assign `scatter_statements` to a list of the numbers for each statement below that can be inferred from this scatter diagram for 1960. \n",
    "1. The `europe_central_asia` region had the lowest child mortality rate.\n",
    "1. The lowest child mortality rate of any country was from an `east_asian_pacific` country.\n",
    "1. Most countries had a fertility rate above 5.\n",
    "1. There was an association between child mortality and fertility.\n",
    "1. The two largest countries by population also had the two highest child mortality rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "deletable": false
   },
   "outputs": [],
   "source": [
    "scatter_statements = make_array(2,4,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 1\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.grade('q1_14')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false
   },
   "source": [
    "The result of the cell below is interactive. Drag the slider to the right to see how countries have changed over time. You'll find that the great divide between so-called \"Western\" and \"developing\" countries that existed in the 1960's has nearly disappeared. This shift in fertility rates is the reason that the global population is expected to grow more slowly in the 21st century than it did in the 19th and 20th centuries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "deletable": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/app/venv/lib/python3.6/site-packages/datascience/tables.py:192: FutureWarning: Implicit column method lookup is deprecated.\n",
      "  warnings.warn(\"Implicit column method lookup is deprecated.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a2e2a98eb6484206b4b18f97d68c561d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another notebook frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "interactive(children=(IntSlider(value=1960, description='year', max=2015, min=1960), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "\n",
    "# This part takes a few minutes to run because it \n",
    "# computes 55 tables in advance: one for each year.\n",
    "Table().with_column('Year', np.arange(1960, 2016)).apply(stats_relabeled, 'Year')\n",
    "\n",
    "_ = widgets.interact(fertilty_vs_child_mortality, \n",
    "                     year=widgets.IntSlider(min=1960, max=2015, value=1960))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Now is a great time to take a break and watch the same data presented by [Hans Rosling in a 2010 TEDx talk](https://www.gapminder.org/videos/reducing-child-mortality-a-moral-and-environmental-imperative) with smoother animation and witty commentary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "## 2. Global Poverty\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "In 1800, 85% of the world's 1 billion people lived in *extreme poverty*, defined by the United Nations as \"a condition characterized by severe deprivation of basic human needs, including food, safe drinking water, sanitation facilities, health, shelter, education and information.\" A common measure of extreme poverty is a person living on less than \\$1.25 per day.\n",
    "\n",
    "In 2015, the proportion of people living in extreme poverty was estimated to be 12%. Although the world rate of extreme poverty has declined consistently for hundreds of years, the number of people living in extreme poverty is still close to 1 billion. The United Nations recently adopted an [ambitious goal](http://www.un.org/sustainabledevelopment/poverty/): \"By 2030, eradicate extreme poverty for all people everywhere.\"\n",
    "In this section, we will examine extreme poverty trends around the world."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "First, load the population and poverty rate by country and year and the country descriptions. While the `population` table has values for every recent year for many countries, the `poverty` table only includes certain years for each country in which a measurement of the rate of extreme poverty was available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>geo</th> <th>time</th> <th>extreme_poverty_percent_people_below_125_a_day</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>alb </td> <td>1996</td> <td>0.2                                           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>alb </td> <td>2002</td> <td>0.73                                          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>alb </td> <td>2004</td> <td>0.53                                          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (1096 rows omitted)</p"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "population = Table.read_table('population.csv')\n",
    "countries = Table.read_table('countries.csv').where('country', are.contained_in(population.group('geo').column(0)))\n",
    "poverty = Table.read_table('poverty.csv')\n",
    "poverty.show(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 1.** Assign `latest` to a three-column table with one row for each country that appears in the `poverty` table. The first column should contain the 3-letter code for the country. The second column should contain the *most recent year* for which an extreme poverty rate is available for the country. The third column should contain the poverty rate in that year. **Do not change the last line, so that the labels of your table are set correctly.**\n",
    "\n",
    "*Hint*: the `first` function may be helpful, but you are not required to use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>geo</th> <th>time list</th> <th>extreme_poverty_percent_people_below_125_a_day list</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>ago </td> <td>[2000, 2009]                                                </td> <td>[54.310000000000002, 43.369999999999997]                    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>alb </td> <td>[1996, 2002, 2004, 2005, 2008, 2012]                        </td> <td>[0.20000000000000001, 0.72999999999999998, 0.53000000000 ...</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>arg </td> <td>[1986, 1987, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1 ...</td> <td>[0.0, 0.0, 0.96999999999999997, 1.77, 2.0499999999999998 ...</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>arm </td> <td>[1996, 1999, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2 ...</td> <td>[17.5, 16.760000000000002, 18.829999999999998, 14.5, 10. ...</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>aus </td> <td>[1981, 1985, 1989, 1995, 2001, 2003]                        </td> <td>[0.67000000000000004, 0.67000000000000004, 0.68000000000 ...</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>aut </td> <td>[1987, 1994, 1995, 1997, 2000, 2004]                        </td> <td>[0.0, 0.40000000000000002, 1.0, 0.34000000000000002, 0.4 ...</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>aze </td> <td>[1995, 2001, 2002, 2003, 2004, 2005, 2008]                  </td> <td>[25.23, 6.2699999999999996, 0.02, 0.040000000000000001,  ...</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>bdi </td> <td>[1992, 1998, 2006]                                          </td> <td>[84.239999999999995, 86.430000000000007, 81.319999999999993]</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>bel </td> <td>[1985, 1988, 1992, 1995, 1997, 2000]                        </td> <td>[0.35999999999999999, 0.67000000000000004, 0.82999999999 ...</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>ben </td> <td>[2003, 2012]                                                </td> <td>[47.329999999999998, 51.609999999999999]                    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (135 rows omitted)</p"
      ],
      "text/plain": [
       "geo  | time list                                                    | extreme_poverty_percent_people_below_125_a_day list\n",
       "ago  | [2000, 2009]                                                 | [54.310000000000002, 43.369999999999997]\n",
       "alb  | [1996, 2002, 2004, 2005, 2008, 2012]                         | [0.20000000000000001, 0.72999999999999998, 0.53000000000 ...\n",
       "arg  | [1986, 1987, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1 ... | [0.0, 0.0, 0.96999999999999997, 1.77, 2.0499999999999998 ...\n",
       "arm  | [1996, 1999, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2 ... | [17.5, 16.760000000000002, 18.829999999999998, 14.5, 10. ...\n",
       "aus  | [1981, 1985, 1989, 1995, 2001, 2003]                         | [0.67000000000000004, 0.67000000000000004, 0.68000000000 ...\n",
       "aut  | [1987, 1994, 1995, 1997, 2000, 2004]                         | [0.0, 0.40000000000000002, 1.0, 0.34000000000000002, 0.4 ...\n",
       "aze  | [1995, 2001, 2002, 2003, 2004, 2005, 2008]                   | [25.23, 6.2699999999999996, 0.02, 0.040000000000000001,  ...\n",
       "bdi  | [1992, 1998, 2006]                                           | [84.239999999999995, 86.430000000000007, 81.319999999999993]\n",
       "bel  | [1985, 1988, 1992, 1995, 1997, 2000]                         | [0.35999999999999999, 0.67000000000000004, 0.82999999999 ...\n",
       "ben  | [2003, 2012]                                                 | [47.329999999999998, 51.609999999999999]\n",
       "... (135 rows omitted)"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "def first(values):\n",
    "    return values.item(0)\n",
    "\n",
    "poverty.group('geo',list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>geo</th> <th>time</th> <th>poverty_percent</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>ago </td> <td>2009</td> <td>43.37          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>alb </td> <td>2012</td> <td>0.46           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>arg </td> <td>2011</td> <td>1.41           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>arm </td> <td>2012</td> <td>1.75           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>aus </td> <td>2003</td> <td>1.36           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>aut </td> <td>2004</td> <td>0.34           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>aze </td> <td>2008</td> <td>0.31           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>bdi </td> <td>2006</td> <td>81.32          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>bel </td> <td>2000</td> <td>0.5            </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>ben </td> <td>2012</td> <td>51.61          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (135 rows omitted)</p"
      ],
      "text/plain": [
       "geo  | time | poverty_percent\n",
       "ago  | 2009 | 43.37\n",
       "alb  | 2012 | 0.46\n",
       "arg  | 2011 | 1.41\n",
       "arm  | 2012 | 1.75\n",
       "aus  | 2003 | 1.36\n",
       "aut  | 2004 | 0.34\n",
       "aze  | 2008 | 0.31\n",
       "bdi  | 2006 | 81.32\n",
       "bel  | 2000 | 0.5\n",
       "ben  | 2012 | 51.61\n",
       "... (135 rows omitted)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def first(values):\n",
    "    return values.item(0)\n",
    "\n",
    "latest = poverty.sort('time', descending=True).group('geo',first)\n",
    "\n",
    "latest.relabel(0, 'geo').relabel(1, 'time').relabel(2, 'poverty_percent') # You should *not* change this line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 2\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.grade('q2_1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 2.** Using both `latest` and `population`, create a four-column table called `recent` with one row for each country in `latest`. The four columns should have the following labels and contents:\n",
    "1. `geo` contains the 3-letter country code,\n",
    "1. `poverty_percent` contains the most recent poverty percent,\n",
    "1. `population_total` contains the population of the country in 2010,\n",
    "1. `poverty_total` contains the number of people in poverty **rounded to the nearest integer**, based on the 2010 population and most recent poverty rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>geo</th> <th>poverty_percent</th> <th>population_total</th> <th>poverty_total</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>ago </td> <td>43.37          </td> <td>21219954        </td> <td>9.20309e+06  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>alb </td> <td>0.46           </td> <td>2901883         </td> <td>13349        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>arg </td> <td>1.41           </td> <td>41222875        </td> <td>581243       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>arm </td> <td>1.75           </td> <td>2963496         </td> <td>51861        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>aus </td> <td>1.36           </td> <td>22162863        </td> <td>301415       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>aut </td> <td>0.34           </td> <td>8391986         </td> <td>28533        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>aze </td> <td>0.31           </td> <td>9099893         </td> <td>28210        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>bdi </td> <td>81.32          </td> <td>9461117         </td> <td>7.69378e+06  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>bel </td> <td>0.5            </td> <td>10929978        </td> <td>54650        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>ben </td> <td>51.61          </td> <td>9509798         </td> <td>4.90801e+06  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (135 rows omitted)</p"
      ],
      "text/plain": [
       "geo  | poverty_percent | population_total | poverty_total\n",
       "ago  | 43.37           | 21219954         | 9.20309e+06\n",
       "alb  | 0.46            | 2901883          | 13349\n",
       "arg  | 1.41            | 41222875         | 581243\n",
       "arm  | 1.75            | 2963496          | 51861\n",
       "aus  | 1.36            | 22162863         | 301415\n",
       "aut  | 0.34            | 8391986          | 28533\n",
       "aze  | 0.31            | 9099893          | 28210\n",
       "bdi  | 81.32           | 9461117          | 7.69378e+06\n",
       "bel  | 0.5             | 10929978         | 54650\n",
       "ben  | 51.61           | 9509798          | 4.90801e+06\n",
       "... (135 rows omitted)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty_and_pop = latest.join('geo', population.where('time', 2010), 'geo').drop('time').drop('time_2')\n",
    "#poverty_and_pop\n",
    "poverty_total = np.round(poverty_and_pop.column(1)/100*poverty_and_pop.column(2))\n",
    "\n",
    "recent = poverty_and_pop.with_column('poverty_total', poverty_total)\n",
    "recent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 3\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.grade('q2_2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 3.** Assuming that the `poverty_total` numbers in the `recent` table describe *all* people in 2010 living in extreme poverty, assign the name `poverty_percent` to the percentage of the world's 2010 population that were living in extreme poverty. You should find a number that is somewhat above the 2015 global estimate of 12%, since many country-specific poverty rates are older than 2015.\n",
    "\n",
    "*Hint*: The sum of the `population_total` column in the `recent` table is not the world population, because only a subset of the world's countries have known poverty rates. Use the `population` table to compute the world's 2010 total population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.299370218520854"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = np.sum(population.where('time', are.equal_to(2010)).column('population_total'))\n",
    "total_poverty = np.sum(recent.column('poverty_total'))\n",
    "poverty_percent = total_poverty/total*100\n",
    "poverty_percent\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 1\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.grade('q2_3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "The `countries` table includes not only the name and region of countries, but also their positions on the globe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>country</th> <th>name</th> <th>world_4region</th> <th>latitude</th> <th>longitude</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>afg      </td> <td>Afghanistan          </td> <td>asia         </td> <td>33      </td> <td>66       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>akr_a_dhe</td> <td>Akrotiri and Dhekelia</td> <td>europe       </td> <td>nan     </td> <td>nan      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>alb      </td> <td>Albania              </td> <td>europe       </td> <td>41      </td> <td>20       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>dza      </td> <td>Algeria              </td> <td>africa       </td> <td>28      </td> <td>3        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>asm      </td> <td>American Samoa       </td> <td>asia         </td> <td>-11.056 </td> <td>-171.082 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>and      </td> <td>Andorra              </td> <td>europe       </td> <td>42.5078 </td> <td>1.52109  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>ago      </td> <td>Angola               </td> <td>africa       </td> <td>-12.5   </td> <td>18.5     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>aia      </td> <td>Anguilla             </td> <td>americas     </td> <td>18.2167 </td> <td>-63.05   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>atg      </td> <td>Antigua and Barbuda  </td> <td>americas     </td> <td>17.05   </td> <td>-61.8    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>arg      </td> <td>Argentina            </td> <td>americas     </td> <td>-34     </td> <td>-64      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (245 rows omitted)</p"
      ],
      "text/plain": [
       "country   | name                  | world_4region | latitude | longitude\n",
       "afg       | Afghanistan           | asia          | 33       | 66\n",
       "akr_a_dhe | Akrotiri and Dhekelia | europe        | nan      | nan\n",
       "alb       | Albania               | europe        | 41       | 20\n",
       "dza       | Algeria               | africa        | 28       | 3\n",
       "asm       | American Samoa        | asia          | -11.056  | -171.082\n",
       "and       | Andorra               | europe        | 42.5078  | 1.52109\n",
       "ago       | Angola                | africa        | -12.5    | 18.5\n",
       "aia       | Anguilla              | americas      | 18.2167  | -63.05\n",
       "atg       | Antigua and Barbuda   | americas      | 17.05    | -61.8\n",
       "arg       | Argentina             | americas      | -34      | -64\n",
       "... (245 rows omitted)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.select('country', 'name', 'world_4region', 'latitude', 'longitude')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 4.** Using both `countries` and `recent`, create a five-column table called `poverty_map` with one row for every country in `recent`.  The four columns should have the following labels and contents:\n",
    "1. `latitude` contains the country's latitude,\n",
    "1. `longitude` contains the country's longitude,\n",
    "1. `name` contains the country's name,\n",
    "1. `region` contains the country's region from the `world_4region` column of `countries`,\n",
    "1. `poverty_total` contains the country's poverty total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>latitude</th> <th>longitude</th> <th>name</th> <th>region</th> <th>poverty_total</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>-12.5   </td> <td>18.5     </td> <td>Angola    </td> <td>africa  </td> <td>9.20309e+06  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>41      </td> <td>20       </td> <td>Albania   </td> <td>europe  </td> <td>13349        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>-34     </td> <td>-64      </td> <td>Argentina </td> <td>americas</td> <td>581243       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>40.25   </td> <td>45       </td> <td>Armenia   </td> <td>europe  </td> <td>51861        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>-25     </td> <td>135      </td> <td>Australia </td> <td>asia    </td> <td>301415       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>47.3333 </td> <td>13.3333  </td> <td>Austria   </td> <td>europe  </td> <td>28533        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>40.5    </td> <td>47.5     </td> <td>Azerbaijan</td> <td>europe  </td> <td>28210        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>-3.5    </td> <td>30       </td> <td>Burundi   </td> <td>africa  </td> <td>7.69378e+06  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>50.75   </td> <td>4.5      </td> <td>Belgium   </td> <td>europe  </td> <td>54650        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>9.5     </td> <td>2.25     </td> <td>Benin     </td> <td>africa  </td> <td>4.90801e+06  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (135 rows omitted)</p"
      ],
      "text/plain": [
       "latitude | longitude | name       | region   | poverty_total\n",
       "-12.5    | 18.5      | Angola     | africa   | 9.20309e+06\n",
       "41       | 20        | Albania    | europe   | 13349\n",
       "-34      | -64       | Argentina  | americas | 581243\n",
       "40.25    | 45        | Armenia    | europe   | 51861\n",
       "-25      | 135       | Australia  | asia     | 301415\n",
       "47.3333  | 13.3333   | Austria    | europe   | 28533\n",
       "40.5     | 47.5      | Azerbaijan | europe   | 28210\n",
       "-3.5     | 30        | Burundi    | africa   | 7.69378e+06\n",
       "50.75    | 4.5       | Belgium    | europe   | 54650\n",
       "9.5      | 2.25      | Benin      | africa   | 4.90801e+06\n",
       "... (135 rows omitted)"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty_map = new_countries=countries.select('country', 'name', 'world_4region', 'latitude', 'longitude')\n",
    "new=recent.drop('poverty_percent').drop('population_total').join('geo', new_countries, 'country').drop('geo')\n",
    "poverty_map=new.relabeled('world_4region', 'region')\n",
    "\n",
    "\n",
    "poverty_map=Table().with_columns('latitude', poverty_map.column(3),'longitude', poverty_map.column(4),'name', poverty_map.column(1),'region', poverty_map.column(2),'poverty_total', poverty_map.column(0))\n",
    "\n",
    "poverty_map\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 2\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.grade('q2_4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Run the cell below to draw a map of the world in which the areas of circles represent the number of people living in extreme poverty. Double-click on the map to zoom in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe srcdoc=\"<!DOCTYPE html>\n",
       "<head>\n",
       "   <meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; />\n",
       "   <link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css&quot; />\n",
       "   <script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.js&quot;></script>\n",
       "\n",
       "   <script src=&quot;https://ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js&quot;></script>\n",
       "\n",
       "   <link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;>\n",
       "   <link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;>\n",
       "   <script src=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js&quot;></script>\n",
       "\n",
       "   <link href=&quot;https://maxcdn.bootstrapcdn.com/font-awesome/4.1.0/css/font-awesome.min.css&quot; rel=&quot;stylesheet&quot;>\n",
       "\n",
       "   <link rel=&quot;stylesheet&quot; href=&quot;https://rawgit.com/lvoogdt/Leaflet.awesome-markers/2.0/develop/dist/leaflet.awesome-markers.css&quot;>\n",
       "   <script src=&quot;https://rawgithub.com/lvoogdt/Leaflet.awesome-markers/2.0/develop/dist/leaflet.awesome-markers.js&quot;></script>\n",
       "\n",
       "\n",
       "   <link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/0.4.0/MarkerCluster.Default.css&quot;>\n",
       "   <link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/0.4.0/MarkerCluster.css&quot;>\n",
       "   <script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/0.4.0/leaflet.markercluster-src.js&quot;></script>\n",
       "   <script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/0.4.0/leaflet.markercluster.js&quot;></script>\n",
       "\n",
       "   <link rel=&quot;stylesheet&quot; href=&quot;https://birdage.github.io/Leaflet.awesome-markers/dist/leaflet.awesome.rotate.css&quot;>\n",
       "\n",
       "   \n",
       "   \n",
       "   \n",
       "   \n",
       "\n",
       "   <style>\n",
       "\n",
       "      html, body {\n",
       "        width: 100%;\n",
       "        height: 100%;\n",
       "        margin: 0;\n",
       "        padding: 0;\n",
       "      }\n",
       "\n",
       "      #map {\n",
       "        position:absolute;\n",
       "        top:0;\n",
       "        bottom:0;\n",
       "        right:0;\n",
       "        left:0;\n",
       "      }\n",
       "\n",
       "   </style>\n",
       "</head>\n",
       "\n",
       "<body>\n",
       "\n",
       "   <div class=&quot;folium-map&quot; id=&quot;folium_f0259ecf3f884b24ab94f5b831380d27&quot; style=&quot;width: 960px; height: 500px&quot;></div>\n",
       "\n",
       "   <script>\n",
       "\n",
       "      \n",
       "\n",
       "      var base_tile = L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {\n",
       "          maxZoom: 17,\n",
       "          minZoom: -1,\n",
       "          attribution: 'Map data (c) <a href=&quot;http://openstreetmap.org&quot;>OpenStreetMap</a> contributors'\n",
       "      });\n",
       "\n",
       "      var baseLayer = {\n",
       "        &quot;Base Layer&quot;: base_tile\n",
       "      };\n",
       "\n",
       "      /*\n",
       "      addition of the wms layers\n",
       "      */\n",
       "\n",
       "      \n",
       "\n",
       "      /*\n",
       "      addition of the tile layers\n",
       "      */\n",
       "      \n",
       "\n",
       "      /*\n",
       "      list of layers to be added\n",
       "      */\n",
       "      var layer_list = {\n",
       "      \n",
       "      };\n",
       "\n",
       "      /*\n",
       "      Bounding box.\n",
       "      */\n",
       "      var southWest = L.latLng(-90, -180),\n",
       "          northEast = L.latLng(90, 180),\n",
       "          bounds = L.latLngBounds(southWest, northEast);\n",
       "\n",
       "      /*\n",
       "      Creates the map and adds the selected layers\n",
       "      */\n",
       "      var map = L.map('folium_f0259ecf3f884b24ab94f5b831380d27', {\n",
       "                                       center:[15.0, 32.17871],\n",
       "                                       zoom: 1,\n",
       "                                       maxBounds: bounds,\n",
       "                                       layers: [base_tile]\n",
       "                                     });\n",
       "\n",
       "      L.control.layers(baseLayer, layer_list).addTo(map);\n",
       "\n",
       "      //cluster group\n",
       "      var clusteredmarkers = L.markerClusterGroup();\n",
       "      //section for adding clustered markers\n",
       "      \n",
       "      //add the clustered markers to the group anyway\n",
       "      map.addLayer(clusteredmarkers);\n",
       "\n",
       "      \n",
       "\n",
       "      \n",
       "      var circle_1 = L.circle([-12.5, 18.5], 136562.687236, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_1.bindPopup(&quot;Angola&quot;);\n",
       "      circle_1._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_1)\n",
       "      \n",
       "      var circle_2 = L.circle([41.0, 20.0], 5201.03159083, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_2.bindPopup(&quot;Albania&quot;);\n",
       "      circle_2._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_2)\n",
       "      \n",
       "      var circle_3 = L.circle([-34.0, -64.0], 34319.740309, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_3.bindPopup(&quot;Argentina&quot;);\n",
       "      circle_3._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_3)\n",
       "      \n",
       "      var circle_4 = L.circle([40.25, 45.0], 10251.4563891, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_4.bindPopup(&quot;Armenia&quot;);\n",
       "      circle_4._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_4)\n",
       "      \n",
       "      var circle_5 = L.circle([-25.0, 135.0], 24714.256844, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_5.bindPopup(&quot;Australia&quot;);\n",
       "      circle_5._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_5)\n",
       "      \n",
       "      var circle_6 = L.circle([47.33333, 13.33333], 7603.94283627, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_6.bindPopup(&quot;Austria&quot;);\n",
       "      circle_6._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_6)\n",
       "      \n",
       "      var circle_7 = L.circle([40.5, 47.5], 7560.78116407, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_7.bindPopup(&quot;Azerbaijan&quot;);\n",
       "      circle_7._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_7)\n",
       "      \n",
       "      var circle_8 = L.circle([-3.5, 30.0], 124863.357017, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_8.bindPopup(&quot;Burundi&quot;);\n",
       "      circle_8._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_8)\n",
       "      \n",
       "      var circle_9 = L.circle([50.75, 4.5], 10523.5000699, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_9.bindPopup(&quot;Belgium&quot;);\n",
       "      circle_9._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_9)\n",
       "      \n",
       "      var circle_10 = L.circle([9.5, 2.25], 99728.1383126, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_10.bindPopup(&quot;Benin&quot;);\n",
       "      circle_10._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_10)\n",
       "      \n",
       "      var circle_11 = L.circle([12.5, -1.66667], 118674.677061, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_11.bindPopup(&quot;Burkina Faso&quot;);\n",
       "      circle_11._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_11)\n",
       "      \n",
       "      var circle_12 = L.circle([24.0, 90.0], 364528.770727, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_12.bindPopup(&quot;Bangladesh&quot;);\n",
       "      circle_12._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_12)\n",
       "      \n",
       "      var circle_13 = L.circle([42.66667, 25.25], 16887.7719458, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_13.bindPopup(&quot;Bulgaria&quot;);\n",
       "      circle_13._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_13)\n",
       "      \n",
       "      var circle_14 = L.circle([44.25, 17.83333], 1763.10348935, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_14.bindPopup(&quot;Bosnia and Herzegovina&quot;);\n",
       "      circle_14._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_14)\n",
       "      \n",
       "      var circle_15 = L.circle([53.0, 28.0], 0.0, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_15.bindPopup(&quot;Belarus&quot;);\n",
       "      circle_15._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_15)\n",
       "      \n",
       "      var circle_16 = L.circle([17.49952, -88.19756], 8920.40384654, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_16.bindPopup(&quot;Belize&quot;);\n",
       "      circle_16._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_16)\n",
       "      \n",
       "      var circle_17 = L.circle([-17.0, -65.0], 40048.2921345, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_17.bindPopup(&quot;Bolivia&quot;);\n",
       "      circle_17._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_17)\n",
       "      \n",
       "      var circle_18 = L.circle([-10.0, -55.0], 122853.043867, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_18.bindPopup(&quot;Brazil&quot;);\n",
       "      circle_18._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_18)\n",
       "      \n",
       "      var circle_19 = L.circle([27.5, 90.5], 5893.80646102, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_19.bindPopup(&quot;Bhutan&quot;);\n",
       "      circle_19._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_19)\n",
       "      \n",
       "      var circle_20 = L.circle([-22.0, 24.0], 23589.9196797, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_20.bindPopup(&quot;Botswana&quot;);\n",
       "      circle_20._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_20)\n",
       "      \n",
       "      var circle_21 = L.circle([7.0, 21.0], 75228.6476403, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_21.bindPopup(&quot;Central African Republic&quot;);\n",
       "      circle_21._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_21)\n",
       "      \n",
       "      var circle_22 = L.circle([60.10867, -113.64258], 15333.7507589, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_22.bindPopup(&quot;Canada&quot;);\n",
       "      circle_22._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_22)\n",
       "      \n",
       "      var circle_23 = L.circle([47.00016, 8.01427], 10310.9804575, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_23.bindPopup(&quot;Switzerland&quot;);\n",
       "      circle_23._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_23)\n",
       "      \n",
       "      var circle_24 = L.circle([-33.45694, -70.64827], 16916.9052488, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_24.bindPopup(&quot;Chile&quot;);\n",
       "      circle_24._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_24)\n",
       "      \n",
       "      var circle_25 = L.circle([35.0, 105.0], 412440.797914, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_25.bindPopup(&quot;China&quot;);\n",
       "      circle_25._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_25)\n",
       "      \n",
       "      var circle_26 = L.circle([8.0, -5.5], 119560.430544, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_26.bindPopup(&quot;Cote d'Ivoire&quot;);\n",
       "      circle_26._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_26)\n",
       "      \n",
       "      var circle_27 = L.circle([6.0, 12.5], 107333.064679, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_27.bindPopup(&quot;Cameroon&quot;);\n",
       "      circle_27._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_27)\n",
       "      \n",
       "      var circle_28 = L.circle([-2.5, 23.5], 342361.299265, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_28.bindPopup(&quot;Congo, Dem. Rep.&quot;);\n",
       "      circle_28._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_28)\n",
       "      \n",
       "      var circle_29 = L.circle([-1.0, 15.5], 52002.2696419, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_29.bindPopup(&quot;Congo, Rep.&quot;);\n",
       "      circle_29._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_29)\n",
       "      \n",
       "      var circle_30 = L.circle([4.0, -73.25], 72378.7827224, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_30.bindPopup(&quot;Colombia&quot;);\n",
       "      circle_30._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_30)\n",
       "      \n",
       "      var circle_31 = L.circle([-12.23333, 44.44553], 25550.9072605, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_31.bindPopup(&quot;Comoros&quot;);\n",
       "      circle_31._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_31)\n",
       "      \n",
       "      var circle_32 = L.circle([16.0, -24.0], 11676.3772082, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_32.bindPopup(&quot;Cape Verde&quot;);\n",
       "      circle_32._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_32)\n",
       "      \n",
       "      var circle_33 = L.circle([10.0, -84.0], 11150.9364176, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_33.bindPopup(&quot;Costa Rica&quot;);\n",
       "      circle_33._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_33)\n",
       "      \n",
       "      var circle_34 = L.circle([49.75, 15.0], 2527.30833434, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_34.bindPopup(&quot;Czech Republic&quot;);\n",
       "      circle_34._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_34)\n",
       "      \n",
       "      var circle_35 = L.circle([51.5, 10.5], 23541.1628016, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_35.bindPopup(&quot;Germany&quot;);\n",
       "      circle_35._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_35)\n",
       "      \n",
       "      var circle_36 = L.circle([11.58901, 43.14503], 17804.8790892, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_36.bindPopup(&quot;Djibouti&quot;);\n",
       "      circle_36._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_36)\n",
       "      \n",
       "      var circle_37 = L.circle([56.0, 10.0], 8681.28578131, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_37.bindPopup(&quot;Denmark&quot;);\n",
       "      circle_37._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_37)\n",
       "      \n",
       "      var circle_38 = L.circle([19.0, -70.66667], 21243.6975139, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_38.bindPopup(&quot;Dominican Republic&quot;);\n",
       "      circle_38._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_38)\n",
       "      \n",
       "      var circle_39 = L.circle([28.0, 3.0], 68256.6103114, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_39.bindPopup(&quot;Algeria&quot;);\n",
       "      circle_39._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_39)\n",
       "      \n",
       "      var circle_40 = L.circle([-1.25, -78.25], 34574.9599144, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_40.bindPopup(&quot;Ecuador&quot;);\n",
       "      circle_40._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_40)\n",
       "      \n",
       "      var circle_41 = L.circle([27.0, 30.0], 52848.8169936, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_41.bindPopup(&quot;Egypt&quot;);\n",
       "      circle_41._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_41)\n",
       "      \n",
       "      var circle_42 = L.circle([40.0, -4.0], 47008.1307291, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_42.bindPopup(&quot;Spain&quot;);\n",
       "      circle_42._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_42)\n",
       "      \n",
       "      var circle_43 = L.circle([59.0, 26.0], 5143.24164563, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_43.bindPopup(&quot;Estonia&quot;);\n",
       "      circle_43._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_43)\n",
       "      \n",
       "      var circle_44 = L.circle([9.0, 39.5], 255497.933376, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_44.bindPopup(&quot;Ethiopia&quot;);\n",
       "      circle_44._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_44)\n",
       "      \n",
       "      var circle_45 = L.circle([64.0, 26.0], 0.0, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_45.bindPopup(&quot;Finland&quot;);\n",
       "      circle_45._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_45)\n",
       "      \n",
       "      var circle_46 = L.circle([-18.0, 178.0], 10122.5546685, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_46.bindPopup(&quot;Fiji&quot;);\n",
       "      circle_46._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_46)\n",
       "      \n",
       "      var circle_47 = L.circle([46.0, 2.0], 20519.1154623, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_47.bindPopup(&quot;France&quot;);\n",
       "      circle_47._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_47)\n",
       "      \n",
       "      var circle_48 = L.circle([6.924, 158.162], 8087.45181676, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_48.bindPopup(&quot;Micronesia, Fed. Sts.&quot;);\n",
       "      circle_48._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_48)\n",
       "      \n",
       "      var circle_49 = L.circle([-1.0, 11.75], 13794.5383603, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_49.bindPopup(&quot;Gabon&quot;);\n",
       "      circle_49._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_49)\n",
       "      \n",
       "      var circle_50 = L.circle([54.75844, -2.69531], 35827.5841344, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_50.bindPopup(&quot;United Kingdom&quot;);\n",
       "      circle_50._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_50)\n",
       "      \n",
       "      var circle_51 = L.circle([41.99998, 43.4999], 34897.2464279, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_51.bindPopup(&quot;Georgia&quot;);\n",
       "      circle_51._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_51)\n",
       "      \n",
       "      var circle_52 = L.circle([8.1, -1.2], 118695.362167, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_52.bindPopup(&quot;Ghana&quot;);\n",
       "      circle_52._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_52)\n",
       "      \n",
       "      var circle_53 = L.circle([10.83333, -10.66667], 95501.1354594, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_53.bindPopup(&quot;Guinea&quot;);\n",
       "      circle_53._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_53)\n",
       "      \n",
       "      var circle_54 = L.circle([13.5, -15.5], 33967.0208158, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_54.bindPopup(&quot;Gambia&quot;);\n",
       "      circle_54._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_54)\n",
       "      \n",
       "      var circle_55 = L.circle([12.0, -15.0], 40241.269094, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_55.bindPopup(&quot;Guinea-Bissau&quot;);\n",
       "      circle_55._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_55)\n",
       "      \n",
       "      var circle_56 = L.circle([39.0, 22.0], 17615.6268657, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_56.bindPopup(&quot;Greece&quot;);\n",
       "      circle_56._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_56)\n",
       "      \n",
       "      var circle_57 = L.circle([14.64072, -90.51327], 63952.8844336, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_57.bindPopup(&quot;Guatemala&quot;);\n",
       "      circle_57._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_57)\n",
       "      \n",
       "      var circle_58 = L.circle([5.0, -59.0], 11524.5763638, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_58.bindPopup(&quot;Guyana&quot;);\n",
       "      circle_58._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_58)\n",
       "      \n",
       "      var circle_59 = L.circle([15.0, -86.5], 50059.5100292, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_59.bindPopup(&quot;Honduras&quot;);\n",
       "      circle_59._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_59)\n",
       "      \n",
       "      var circle_60 = L.circle([45.16667, 15.5], 0.0, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_60.bindPopup(&quot;Croatia&quot;);\n",
       "      circle_60._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_60)\n",
       "      \n",
       "      var circle_61 = L.circle([19.0, -72.41667], 111823.889438, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_61.bindPopup(&quot;Haiti&quot;);\n",
       "      circle_61._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_61)\n",
       "      \n",
       "      var circle_62 = L.circle([47.0, 20.0], 3185.32625173, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_62.bindPopup(&quot;Hungary&quot;);\n",
       "      circle_62._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_62)\n",
       "      \n",
       "      var circle_63 = L.circle([-5.0, 120.0], 281632.579069, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_63.bindPopup(&quot;Indonesia&quot;);\n",
       "      circle_63._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_63)\n",
       "      \n",
       "      var circle_64 = L.circle([22.0, 79.0], 767756.105309, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_64.bindPopup(&quot;India&quot;);\n",
       "      circle_64._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_64)\n",
       "      \n",
       "      var circle_65 = L.circle([53.0, -8.0], 5803.72417419, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_65.bindPopup(&quot;Ireland&quot;);\n",
       "      circle_65._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_65)\n",
       "      \n",
       "      var circle_66 = L.circle([32.0, 53.0], 46709.7172068, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_66.bindPopup(&quot;Iran&quot;);\n",
       "      circle_66._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_66)\n",
       "      \n",
       "      var circle_67 = L.circle([33.0, 44.0], 49454.8472834, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_67.bindPopup(&quot;Iraq&quot;);\n",
       "      circle_67._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_67)\n",
       "      \n",
       "      var circle_68 = L.circle([31.5, 34.75], 7657.85052534, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_68.bindPopup(&quot;Israel&quot;);\n",
       "      circle_68._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_68)\n",
       "      \n",
       "      var circle_69 = L.circle([42.83333, 12.83333], 40374.8957465, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_69.bindPopup(&quot;Italy&quot;);\n",
       "      circle_69._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_69)\n",
       "      \n",
       "      var circle_70 = L.circle([18.16667, -77.25], 3651.27941189, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_70.bindPopup(&quot;Jamaica&quot;);\n",
       "      circle_70._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_70)\n",
       "      \n",
       "      var circle_71 = L.circle([31.0, 36.0], 3250.50350411, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_71.bindPopup(&quot;Jordan&quot;);\n",
       "      circle_71._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_71)\n",
       "      \n",
       "      var circle_72 = L.circle([35.68536, 139.75309], 30050.1728892, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_72.bindPopup(&quot;Japan&quot;);\n",
       "      circle_72._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_72)\n",
       "      \n",
       "      var circle_73 = L.circle([48.0, 68.0], 4453.15417008, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_73.bindPopup(&quot;Kazakhstan&quot;);\n",
       "      circle_73._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_73)\n",
       "      \n",
       "      var circle_74 = L.circle([1.0, 38.0], 188262.949931, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_74.bindPopup(&quot;Kenya&quot;);\n",
       "      circle_74._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_74)\n",
       "      \n",
       "      var circle_75 = L.circle([41.5, 75.0], 23787.7388581, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_75.bindPopup(&quot;Kyrgyz Republic&quot;);\n",
       "      circle_75._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_75)\n",
       "      \n",
       "      var circle_76 = L.circle([13.0, 105.0], 54085.3365405, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_76.bindPopup(&quot;Cambodia&quot;);\n",
       "      circle_76._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_76)\n",
       "      \n",
       "      var circle_77 = L.circle([18.0, 105.0], 61959.1808307, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_77.bindPopup(&quot;Lao&quot;);\n",
       "      circle_77._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_77)\n",
       "      \n",
       "      var circle_78 = L.circle([6.5, -9.5], 81963.5533472, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_78.bindPopup(&quot;Liberia&quot;);\n",
       "      circle_78._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_78)\n",
       "      \n",
       "      var circle_79 = L.circle([13.88333, -60.96667], 8680.23530809, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_79.bindPopup(&quot;St. Lucia&quot;);\n",
       "      circle_79._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_79)\n",
       "      \n",
       "      var circle_80 = L.circle([7.75, 80.75], 41018.1287792, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_80.bindPopup(&quot;Sri Lanka&quot;);\n",
       "      circle_80._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_80)\n",
       "      \n",
       "      var circle_81 = L.circle([-29.5, 28.25], 47859.8999688, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_81.bindPopup(&quot;Lesotho&quot;);\n",
       "      circle_81._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_81)\n",
       "      \n",
       "      var circle_82 = L.circle([55.41667, 24.0], 7247.40654305, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_82.bindPopup(&quot;Lithuania&quot;);\n",
       "      circle_82._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_82)\n",
       "      \n",
       "      var circle_83 = L.circle([57.0, 25.0], 6732.68676963, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_83.bindPopup(&quot;Latvia&quot;);\n",
       "      circle_83._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_83)\n",
       "      \n",
       "      var circle_84 = L.circle([32.0, -6.0], 40891.8328728, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_84.bindPopup(&quot;Morocco&quot;);\n",
       "      circle_84._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_84)\n",
       "      \n",
       "      var circle_85 = L.circle([47.25, 28.58333], 4363.05214073, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_85.bindPopup(&quot;Moldova&quot;);\n",
       "      circle_85._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_85)\n",
       "      \n",
       "      var circle_86 = L.circle([-20.0, 47.0], 193517.887366, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_86.bindPopup(&quot;Madagascar&quot;);\n",
       "      circle_86._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_86)\n",
       "      \n",
       "      var circle_87 = L.circle([3.2, 73.0], 3158.17309813, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_87.bindPopup(&quot;Maldives&quot;);\n",
       "      circle_87._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_87)\n",
       "      \n",
       "      var circle_88 = L.circle([19.42847, -99.12766], 49757.4659017, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_88.bindPopup(&quot;Mexico&quot;);\n",
       "      circle_88._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_88)\n",
       "      \n",
       "      var circle_89 = L.circle([41.66667, 21.75], 3420.90583189, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_89.bindPopup(&quot;Macedonia, FYR&quot;);\n",
       "      circle_89._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_89)\n",
       "      \n",
       "      var circle_90 = L.circle([18.0, -2.0], 124720.320797, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_90.bindPopup(&quot;Mali&quot;);\n",
       "      circle_90._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_90)\n",
       "      \n",
       "      var circle_91 = L.circle([42.75, 19.25], 1626.80955147, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_91.bindPopup(&quot;Montenegro&quot;);\n",
       "      circle_91._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_91)\n",
       "      \n",
       "      var circle_92 = L.circle([-18.25, 35.0], 172977.669795, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_92.bindPopup(&quot;Mozambique&quot;);\n",
       "      circle_92._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_92)\n",
       "      \n",
       "      var circle_93 = L.circle([20.25, -10.5], 41293.6386853, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_93.bindPopup(&quot;Mauritania&quot;);\n",
       "      circle_93._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_93)\n",
       "      \n",
       "      var circle_94 = L.circle([-13.5, 34.0], 146960.644374, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_94.bindPopup(&quot;Malawi&quot;);\n",
       "      circle_94._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_94)\n",
       "      \n",
       "      var circle_95 = L.circle([2.5, 112.5], 0.0, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_95.bindPopup(&quot;Malaysia&quot;);\n",
       "      circle_95._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_95)\n",
       "      \n",
       "      var circle_96 = L.circle([-22.0, 17.0], 32348.3038486, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_96.bindPopup(&quot;Namibia&quot;);\n",
       "      circle_96._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_96)\n",
       "      \n",
       "      var circle_97 = L.circle([18.0, 9.0], 116074.143053, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_97.bindPopup(&quot;Niger&quot;);\n",
       "      circle_97._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_97)\n",
       "      \n",
       "      var circle_98 = L.circle([10.0, 8.0], 447655.449921, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_98.bindPopup(&quot;Nigeria&quot;);\n",
       "      circle_98._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_98)\n",
       "      \n",
       "      var circle_99 = L.circle([13.0, -85.0], 31511.1032196, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_99.bindPopup(&quot;Nicaragua&quot;);\n",
       "      circle_99._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_99)\n",
       "      \n",
       "      var circle_100 = L.circle([52.25, 5.75], 10860.8527288, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_100.bindPopup(&quot;Netherlands&quot;);\n",
       "      circle_100._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_100)\n",
       "      \n",
       "      var circle_101 = L.circle([62.0, 10.0], 8149.10609717, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_101.bindPopup(&quot;Norway&quot;);\n",
       "      circle_101._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_101)\n",
       "      \n",
       "      var circle_102 = L.circle([28.0, 84.0], 113706.965676, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_102.bindPopup(&quot;Nepal&quot;);\n",
       "      circle_102._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_102)\n",
       "      \n",
       "      var circle_103 = L.circle([30.0, 70.0], 209522.365744, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_103.bindPopup(&quot;Pakistan&quot;);\n",
       "      circle_103._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_103)\n",
       "      \n",
       "      var circle_104 = L.circle([8.9936, -79.51973], 17109.4450796, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_104.bindPopup(&quot;Panama&quot;);\n",
       "      circle_104._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_104)\n",
       "      \n",
       "      var circle_105 = L.circle([-10.0, -75.25], 41475.6193809, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_105.bindPopup(&quot;Peru&quot;);\n",
       "      circle_105._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_105)\n",
       "      \n",
       "      var circle_106 = L.circle([13.0, 122.0], 189067.369579, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_106.bindPopup(&quot;Philippines&quot;);\n",
       "      circle_106._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_106)\n",
       "      \n",
       "      var circle_107 = L.circle([-6.0, 147.0], 70471.3358896, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_107.bindPopup(&quot;Papua New Guinea&quot;);\n",
       "      circle_107._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_107)\n",
       "      \n",
       "      var circle_108 = L.circle([52.0, 20.0], 0.0, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_108.bindPopup(&quot;Poland&quot;);\n",
       "      circle_108._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_108)\n",
       "      \n",
       "      var circle_109 = L.circle([-23.33333, -58.0], 19526.6446646, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_109.bindPopup(&quot;Paraguay&quot;);\n",
       "      circle_109._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_109)\n",
       "      \n",
       "      var circle_110 = L.circle([31.92157, 35.20329], 2568.26966168, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_110.bindPopup(&quot;Palestine&quot;);\n",
       "      circle_110._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_110)\n",
       "      \n",
       "      var circle_111 = L.circle([46.0, 25.0], 0.0, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_111.bindPopup(&quot;Romania&quot;);\n",
       "      circle_111._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_111)\n",
       "      \n",
       "      var circle_112 = L.circle([60.0, 100.0], 9328.92370414, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_112.bindPopup(&quot;Russia&quot;);\n",
       "      circle_112._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_112)\n",
       "      \n",
       "      var circle_113 = L.circle([-2.0, 30.0], 114654.054509, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_113.bindPopup(&quot;Rwanda&quot;);\n",
       "      circle_113._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_113)\n",
       "      \n",
       "      var circle_114 = L.circle([16.0, 30.0], 120376.255962, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_114.bindPopup(&quot;Sudan&quot;);\n",
       "      circle_114._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_114)\n",
       "      \n",
       "      var circle_115 = L.circle([14.5, -14.25], 94566.2511758, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_115.bindPopup(&quot;Senegal&quot;);\n",
       "      circle_115._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_115)\n",
       "      \n",
       "      var circle_116 = L.circle([8.5, -11.5], 81413.8502133, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_116.bindPopup(&quot;Sierra Leone&quot;);\n",
       "      circle_116._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_116)\n",
       "      \n",
       "      var circle_117 = L.circle([13.83333, -88.91667], 17594.7355217, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_117.bindPopup(&quot;El Salvador&quot;);\n",
       "      circle_117._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_117)\n",
       "      \n",
       "      var circle_118 = L.circle([44.81892, 20.45998], 3029.80184798, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_118.bindPopup(&quot;Serbia&quot;);\n",
       "      circle_118._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_118)\n",
       "      \n",
       "      var circle_119 = L.circle([1.0, 7.0], 12277.3571456, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_119.bindPopup(&quot;Sao Tome and Principe&quot;);\n",
       "      circle_119._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_119)\n",
       "      \n",
       "      var circle_120 = L.circle([4.0, -56.0], 12773.6293869, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_120.bindPopup(&quot;Suriname&quot;);\n",
       "      circle_120._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_120)\n",
       "      \n",
       "      var circle_121 = L.circle([48.66667, 19.5], 5733.29036394, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_121.bindPopup(&quot;Slovak Republic&quot;);\n",
       "      circle_121._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_121)\n",
       "      \n",
       "      var circle_122 = L.circle([46.08333, 15.0], 0.0, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_122.bindPopup(&quot;Slovenia&quot;);\n",
       "      circle_122._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_122)\n",
       "      \n",
       "      var circle_123 = L.circle([62.0, 15.0], 8157.43222889, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_123.bindPopup(&quot;Sweden&quot;);\n",
       "      circle_123._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_123)\n",
       "      \n",
       "      var circle_124 = L.circle([-26.5, 31.5], 30825.3831308, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_124.bindPopup(&quot;Swaziland&quot;);\n",
       "      circle_124._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_124)\n",
       "      \n",
       "      var circle_125 = L.circle([-4.58333, 55.66667], 687.136606341, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_125.bindPopup(&quot;Seychelles&quot;);\n",
       "      circle_125._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_125)\n",
       "      \n",
       "      var circle_126 = L.circle([35.0, 38.0], 26795.6430901, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_126.bindPopup(&quot;Syria&quot;);\n",
       "      circle_126._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_126)\n",
       "      \n",
       "      var circle_127 = L.circle([15.0, 19.0], 93829.1808515, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_127.bindPopup(&quot;Chad&quot;);\n",
       "      circle_127._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_127)\n",
       "      \n",
       "      var circle_128 = L.circle([8.66667, 1.08333], 82424.9298606, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_128.bindPopup(&quot;Togo&quot;);\n",
       "      circle_128._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_128)\n",
       "      \n",
       "      var circle_129 = L.circle([15.5, 101.0], 20468.3404858, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_129.bindPopup(&quot;Thailand&quot;);\n",
       "      circle_129._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_129)\n",
       "      \n",
       "      var circle_130 = L.circle([39.0, 71.0], 31528.3009816, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_130.bindPopup(&quot;Tajikistan&quot;);\n",
       "      circle_130._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_130)\n",
       "      \n",
       "      var circle_131 = L.circle([39.75, 59.66667], 50357.8513436, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_131.bindPopup(&quot;Turkmenistan&quot;);\n",
       "      circle_131._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_131)\n",
       "      \n",
       "      var circle_132 = L.circle([-8.83333, 125.75], 27330.8660938, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_132.bindPopup(&quot;Timor-Leste&quot;);\n",
       "      circle_132._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_132)\n",
       "      \n",
       "      var circle_133 = L.circle([11.0, -61.0], 10581.0151451, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_133.bindPopup(&quot;Trinidad and Tobago&quot;);\n",
       "      circle_133._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_133)\n",
       "      \n",
       "      var circle_134 = L.circle([34.0, 9.0], 12630.9277475, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_134.bindPopup(&quot;Tunisia&quot;);\n",
       "      circle_134._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_134)\n",
       "      \n",
       "      var circle_135 = L.circle([39.0, 35.0], 10827.0289843, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_135.bindPopup(&quot;Turkey&quot;);\n",
       "      circle_135._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_135)\n",
       "      \n",
       "      var circle_136 = L.circle([-6.0, 35.0], 200550.279241, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_136.bindPopup(&quot;Tanzania&quot;);\n",
       "      circle_136._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_136)\n",
       "      \n",
       "      var circle_137 = L.circle([1.25, 32.5], 159306.704552, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_137.bindPopup(&quot;Uganda&quot;);\n",
       "      circle_137._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_137)\n",
       "      \n",
       "      var circle_138 = L.circle([49.0, 32.0], 0.0, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'black',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_138.bindPopup(&quot;Ukraine&quot;);\n",
       "      circle_138._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_138)\n",
       "      \n",
       "      var circle_139 = L.circle([-33.0, -56.0], 4604.17398038, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_139.bindPopup(&quot;Uruguay&quot;);\n",
       "      circle_139._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_139)\n",
       "      \n",
       "      var circle_140 = L.circle([39.76, -98.5], 102404.124478, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_140.bindPopup(&quot;United States&quot;);\n",
       "      circle_140._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_140)\n",
       "      \n",
       "      var circle_141 = L.circle([8.0, -66.0], 62415.0089666, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'green',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_141.bindPopup(&quot;Venezuela&quot;);\n",
       "      circle_141._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_141)\n",
       "      \n",
       "      var circle_142 = L.circle([16.16667, 107.83333], 66097.1072665, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_142.bindPopup(&quot;Vietnam&quot;);\n",
       "      circle_142._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_142)\n",
       "      \n",
       "      var circle_143 = L.circle([15.5, 47.5], 68378.0462447, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'red',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_143.bindPopup(&quot;Yemen&quot;);\n",
       "      circle_143._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_143)\n",
       "      \n",
       "      var circle_144 = L.circle([-29.0, 24.0], 99267.3149674, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_144.bindPopup(&quot;South Africa&quot;);\n",
       "      circle_144._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_144)\n",
       "      \n",
       "      var circle_145 = L.circle([-14.33333, 28.5], 144776.357535, {\n",
       "                            color: 'None',\n",
       "                            fillColor: 'blue',\n",
       "                            fillOpacity: 0.6\n",
       "                            });\n",
       "      circle_145.bindPopup(&quot;Zambia&quot;);\n",
       "      circle_145._popup.options.maxWidth = 300;\n",
       "      map.addLayer(circle_145)\n",
       "      \n",
       "\n",
       "      \n",
       "\n",
       "      \n",
       "\n",
       "      \n",
       "\n",
       "      \n",
       "\n",
       "\n",
       "\n",
       "   </script>\n",
       "\n",
       "</body>\" style=\"width: 960px; height: 500px\"; border: none\"></iframe>"
      ],
      "text/plain": [
       "<datascience.maps.Map at 0x7fb106fc2828>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors = {'africa': 'blue', 'europe': 'black', 'asia': 'red', 'americas': 'green'}\n",
    "scaled = poverty_map.with_column(\n",
    "    'poverty_total', 2e4 * poverty_map.column('poverty_total'),\n",
    "    'region', poverty_map.apply(colors.get, 'region')\n",
    ")\n",
    "Circle.map_table(scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Although people live in extreme poverty throughout the world (with more than 5 million in the United States), the largest numbers are in Asia and Africa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 5.** Assign `largest` to a two-column table with the `name` (not the 3-letter code) and `poverty_total` of the 10 countries with the largest number of people living in extreme poverty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>name</th> <th>poverty_total</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>India           </td> <td>2.90882e+08  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Nigeria         </td> <td>9.88912e+07  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>China           </td> <td>8.39446e+07  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Bangladesh      </td> <td>6.55743e+07  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Congo, Dem. Rep.</td> <td>5.78414e+07  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Indonesia       </td> <td>3.91413e+07  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Ethiopia        </td> <td>3.2214e+07   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Pakistan        </td> <td>2.16636e+07  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Tanzania        </td> <td>1.9848e+07   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Madagascar      </td> <td>1.84804e+07  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "name             | poverty_total\n",
       "India            | 2.90882e+08\n",
       "Nigeria          | 9.88912e+07\n",
       "China            | 8.39446e+07\n",
       "Bangladesh       | 6.55743e+07\n",
       "Congo, Dem. Rep. | 5.78414e+07\n",
       "Indonesia        | 3.91413e+07\n",
       "Ethiopia         | 3.2214e+07\n",
       "Pakistan         | 2.16636e+07\n",
       "Tanzania         | 1.9848e+07\n",
       "Madagascar       | 1.84804e+07"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new = countries.select(\"name\",\"country\").join(\"country\", recent, \"geo\")\n",
    "largest=new.drop(\"country\", \"poverty_percent\", \"population_total\")\n",
    "\n",
    "name=largest.sort(\"poverty_total\", descending=True).column('name')[:10]\n",
    "pov=largest.sort(\"poverty_total\", descending=True).column('poverty_total')[:10]\n",
    "\n",
    "largest=Table().with_columns('name', name, \"poverty_total\", pov)\n",
    "largest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Running tests\n",
      "\n",
      "---------------------------------------------------------------------\n",
      "Test summary\n",
      "    Passed: 3\n",
      "    Failed: 0\n",
      "[ooooooooook] 100.0% passed\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = ok.grade('q2_5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**Question 6.** Write a function called `poverty_timeline` that takes the name of a country as its argument. It should draw a line plot of the number of people living in poverty in that country with time on the horizontal axis. The line plot should have a point for each row in the `poverty` table for that country. To compute the population living in poverty from a poverty percentage, multiply by the population of the country **in that year**.\n",
    "\n",
    "*Hint*: The names within the `poverty_timeline` function correspond to our staff solution, but you don't need to use them. Any way that you want to draw the plot is fine, as long as it generates the correct graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>geo</th> <th>time</th> <th>extreme_poverty_percent_people_below_125_a_day</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>ind </td> <td>1978</td> <td>65.89                                         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>isr </td> <td>1979</td> <td>0                                             </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>pan </td> <td>1979</td> <td>7.98                                          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>col </td> <td>1980</td> <td>15.45                                         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>mdg </td> <td>1980</td> <td>94.64                                         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>esp </td> <td>1980</td> <td>0.67                                          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>aus </td> <td>1981</td> <td>0.67                                          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>bra </td> <td>1981</td> <td>13.64                                         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>can </td> <td>1981</td> <td>0.68                                          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>chn </td> <td>1981</td> <td>84.27                                         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (1089 rows omitted)</p"
      ],
      "text/plain": [
       "geo  | time | extreme_poverty_percent_people_below_125_a_day\n",
       "ind  | 1978 | 65.89\n",
       "isr  | 1979 | 0\n",
       "pan  | 1979 | 7.98\n",
       "col  | 1980 | 15.45\n",
       "mdg  | 1980 | 94.64\n",
       "esp  | 1980 | 0.67\n",
       "aus  | 1981 | 0.67\n",
       "bra  | 1981 | 13.64\n",
       "can  | 1981 | 0.68\n",
       "chn  | 1981 | 84.27\n",
       "... (1089 rows omitted)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poverty.sort('time', descending=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "deletable": false,
    "manual_grade": true,
    "manual_problem_id": "global_poverty_6"
   },
   "outputs": [],
   "source": [
    "def population_for_country_in_year(row_of_poverty_table):\n",
    "    \"\"\"Optional: Define a function to return the population \n",
    "    of a country in a year using a row from the poverty table.\"\"\"\n",
    "    return population.where('geo',row_of_poverty_table.column('geo'\n",
    "    ).item(0)).where('time', row_of_poverty_table.column('time'\n",
    "    ).item(0)).column('population_total').item(0)\n",
    "    \n",
    "\n",
    "def poverty_timeline(country):\n",
    "    \"\"\"Draw a timeline of people living in extreme poverty in a country.\"\"\"\n",
    "    geo = countries.where('name', country).column('geo').item(0)\n",
    "    country_poverty = poverty.where('geo', geo)\n",
    "    Table().with_columns(\n",
    "    'year', np.arange(1978,2013,1) ,\n",
    "    'population in poverty', country_poverty*poverty.column(2)\n",
    " ).plot\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Finally, draw the timelines below to see how the world is changing. You can check your work by comparing your graphs to the ones on [gapminder.org](https://goo.gl/lPujuh)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The column \"geo\" is not in the table. The table contains these columns: country, gwid, name, world_6region, income_groups, landlocked, g77_and_oecd_countries, main_religion_2008, gapminder_list, alternative_1, alternative_2, alternative_3, alternative_4_cdiac, pandg, god_id, alt_5, upper_case_name, code, number, arb1, arb2, arb3, arb4, arb5, arb6, is--country, world_4region, latitude, longitude",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-17054a92521c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpoverty_timeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'India'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-37-1398e0b5dbe8>\u001b[0m in \u001b[0;36mpoverty_timeline\u001b[0;34m(country)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpoverty_timeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcountry\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;34m\"\"\"Draw a timeline of people living in extreme poverty in a country.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m     \u001b[0mgeo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcountries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcountry\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'geo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mcountry_poverty\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpoverty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'geo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     Table().with_columns(\n",
      "\u001b[0;32m/srv/app/venv/lib/python3.6/site-packages/datascience/tables.py\u001b[0m in \u001b[0;36mcolumn\u001b[0;34m(self, index_or_label)\u001b[0m\n\u001b[1;32m    273\u001b[0m                 \u001b[0;34m'The column \"{}\" is not in the table. The table contains '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m                 \u001b[0;34m'these columns: {}'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m                 \u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_or_label\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m', '\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    276\u001b[0m             )\n\u001b[1;32m    277\u001b[0m         if (isinstance(index_or_label, int)\n",
      "\u001b[0;31mValueError\u001b[0m: The column \"geo\" is not in the table. The table contains these columns: country, gwid, name, world_6region, income_groups, landlocked, g77_and_oecd_countries, main_religion_2008, gapminder_list, alternative_1, alternative_2, alternative_3, alternative_4_cdiac, pandg, god_id, alt_5, upper_case_name, code, number, arb1, arb2, arb3, arb4, arb5, arb6, is--country, world_4region, latitude, longitude"
     ]
    }
   ],
   "source": [
    "poverty_timeline('India')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "poverty_timeline('Nigeria')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true,
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "poverty_timeline('China')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "poverty_timeline('United States')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "Although the number of people living in extreme poverty has been increasing in Nigeria and the United States, the massive decreases in China and India have shaped the overall trend that extreme poverty is decreasing worldwide, both in percentage and in absolute number. \n",
    "\n",
    "To learn more, watch [Hans Rosling in a 2015 film](https://www.gapminder.org/videos/dont-panic-end-poverty/) about the UN goal of eradicating extreme poverty from the world. \n",
    "\n",
    "Below, we've also added an interactive dropdown menu for you to visualize `poverty_timeline` graphs for other countries. Note that each dropdown menu selection may take a few seconds to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Just run this cell\n",
    "\n",
    "all_countries = poverty_map.column('name')\n",
    "_ = widgets.interact(poverty_timeline, country=list(all_countries))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "**You're finished!** Congratulations on mastering data visualization and table manipulation. Time to submit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "_ = ok.submit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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